Restricted Set Research Articles

Deep learning and saliency-based parking IoT classification under different weather conditions

The world’s population is growing at an exponential rate, which has created a slew of new issues in our daily lives. Among these challenges is the difficulty of finding available parking spaces. To overcome this obstacle, an effective system for detecting vacant parking spaces is crucial, particularly in densely populated areas. Even though there are numerous parking detecting systems on the market today, they all have their own set of restrictions. There are primarily two types of parking systems available. The first is a hardware-based system that uses hardware to detect whether a space is vacant or occupied, and the second is a smart parking system that uses cameras to classify parking status. Vision-based parking systems are becoming increasingly popular due to benefits such as flexibility and cost efficiency. However, due to challenging weather conditions and various occlusions caused by objects, such systems are challenging to develop. The limitations of a vision-based parking system must be addressed for it to become more accurate. To date, many researchers have attempted to incorporate deep learning-based parking systems with various algorithms but none of them has focused on reducing the complexity of Convolutional Neural Network (CNN) training through unsupervised pre-processing steps. In this study, an attempt is made to reduce the complexity of CNN by integrating salient features as a pre-processing step for normalizing weather conditions. It has been observed that salient features-based pre-processing makes the system more robust in terms of accuracy and time efficiency.

A validation workflow for treatment wetland performance data

ABSTRACT Treatment wetlands (TWs) effectively remove target pollutants and enhance urban water circularity and resilience. They constitute a prominent solution for urban wastewater treatment, thanks to their adaptability across various types of wastewater, scales and climatic conditions. However, the disparity in TW designs and the focus on a restricted set of variables applicable to research studies impede any comprehensive evaluation and comparison of TW performance. Our study introduces a methodology for data validation, in concurrently establishing a workflow specific to TW. This approach is aimed at defining the scope and relationships within the data, implementing checks and concatenating them into a quality flag, as an initial step towards building reliable statistical models. We underscore the importance of both mobilising comprehensive knowledge and identifying customary, yet implicit, choices intertwined in data processing. As for the application workflow, we collected and analysed data sourced from peer-reviewed papers on horizontal and vertical flow TW. Deficiencies were noted in key data elements like dimensions, concentrations and operational conditions. For the data analysis, relationships are highlighted between variables introduced for modelling purposes. These methodologies and workflows assess the quality of the data, in paving the way towards more dependable statistical models for TW design and implementation.

Adolescents who enter state custody concurrently with a psychiatric hospitalization

Some youth enter state custody concurrently with a psychiatric hospitalization. This can happen when youth have serious emotional disturbances and parents or guardians relinquish custody in order to pay for inpatient psychiatric care. We studied a population of 521 adolescents who entered state custody through the Illinois Department of Children and Family Services (IDCFS) concurrently with a psychiatric hospitalization between our study period from July 2018 to March 2023, and compared them to populations entering state custody in that time period through medical hospitalization and conventional placements. A large majority (79.5 %) of those in the psychiatric hospitalization group entered custody without a finding of parental abuse or neglect or because parents locked out the child, an action some parents take to force the state to take custody Their median length of stay of 56 days suggests that many youth stayed beyond medical necessity. Almost two-thirds went from the psychiatric hospital to a group home or residential treatment center. We discuss how such use of psychiatric hospitalization and congregate care contrasts with policy emphasizing serving youth in the least restrictive setting.

The unconventional resistance protein PTR recognizes the Magnaporthe oryzae effector AVR-Pita in an allele-specific manner.

Blast disease caused by the fungus Magnaporthe oryzae is one of the most devastating rice diseases. Disease resistance genes such as Pi-ta or Pi-ta2 are critical in protecting rice production from blast. Published work reports that Pi-ta codes for a nucleotide-binding and leucine-rich repeat domain protein (NLR) that recognizes the fungal protease-like effector AVR-Pita by direct binding. However, this model was challenged by the recent discovery that Pi-ta2 resistance, which also relies on AVR-Pita detection, is conferred by the unconventional resistance gene Ptr, which codes for a membrane protein with a cytoplasmic armadillo repeat domain. Here, using NLR Pi-ta and Ptr RNAi knockdown and CRISPR/Cas9 knockout mutant rice lines, we found that AVR-Pita recognition relies solely on Ptr and that the NLR Pi-ta has no role in it, indicating that it is not the Pi-ta resistance gene. Different alleles of Ptr confer different recognition specificities. The A allele of Ptr (PtrA) detects all natural sequence variants of the effector and confers Pi-ta2 resistance, while the B allele of Ptr (PtrB) recognizes a restricted set of AVR-Pita alleles and, thereby, confers Pi-ta resistance. Analysis of the natural diversity in AVR-Pita and of mutant and transgenic strains identified one specific polymorphism in the effector sequence that controls escape from PtrB-mediated resistance. Taken together, our work establishes that the M. oryzae effector AVR-Pita is detected in an allele-specific manner by the unconventional rice resistance protein Ptr and that the NLR Pi-ta has no function in Pi-ta resistance and the recognition of AVR-Pita.

Using AI to Support Special Education Teacher Workload

There is a nationwide shortage of special education teachers (SETs) due, in part, to unmanageable workload expectations, which has reached crisis level. SETs are expected to modify, adapt, and accommodate general education curriculum to meet the needs of their students, communicate and collaborate with parents and general education teachers, and progress monitor on IEP goals, to name a few. SETs, especially those in more restrictive self-contained settings, report spending almost half of their time completing non-teaching tasks. One emerging and innovative solution to help SETs accomplish these tasks is using Artificial Intelligence (AI). AI powers many popular educational tools, such as predictive text, adaptive learning platforms, and digital assistants. The launch of ChatGPT, Bard, and other generative pre-trained transformers (GPTs), provides an opportunity to support SETs with some of the paperwork requirements. This is due to the GPTs ability to craft human-like responses via drafting essays, emails, lists, and the like. In this article, we provide step-by-step directions to use ChatGPT. Additionally, we illustrate how GPTs can be used to operationalize, automate, and streamline many of the SET’s non-teaching tasks through specific examples of its use in (1) collaboration, (2) adapting readings, and (3) developing progress monitoring assessments.

ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data

Applications increasingly leverage mixed-modality data, and must jointly search over vector data, such as embedded images, text and video, as well as structured data, such as attributes and keywords. Proposed methods for this hybrid search setting either suffer from poor performance or support a severely restricted set of search predicates (e.g., only small sets of equality predicates), making them impractical for many applications. To address this, we present ACORN, an approach for performant and predicate-agnostic hybrid search. ACORN builds on Hierarchical Navigable Small Worlds (HNSW), a state-of-the-art graph-based approximate nearest neighbor index, and can be implemented efficiently by extending existing HNSW libraries. ACORN introduces the idea of predicate subgraph traversal to emulate a theoretically ideal, but impractical, hybrid search strategy. ACORN's predicate-agnostic construction algorithm is designed to enable this effective search strategy, while supporting a wide array of predicate sets and query semantics. We systematically evaluate ACORN on both prior benchmark datasets, with simple, low-cardinality predicate sets, and complex multi-modal datasets not supported by prior methods. We show that ACORN achieves state-of-the-art performance on all datasets, outperforming prior methods with 2--1,000× higher throughput at a fixed recall. Our code is available at: https://github.com/stanford-futuredata/ACORN.

Integrated Analysis of the Eocene Sakesar Formation: Depositional Environment, Microfacies, Geochemistry, and Reservoir Characteristics in the Potwar Basin, Pakistan

The current study aimed to evaluate the petroleum generation potential of the Sakesar Formation. This study interprets and presents a depositional environment model, microfacies, and geochemical and petrophysical data of the Eocene Sakesar Formation in the Potwar Basin, Pakistan. Twenty well-cutting samples from two wells and six fresh outcrop samples were thoroughly studied. Results of total organic carbon and Rock-Eval pyrolysis of Sakesar Formation sediments show fair to good TOC contents ranging from 1.2-1.67 wt%. S2 values of samples showed fair to good generation potential. Sediments appear mature, having primarily mixed Type II-III kerogen with good oil/gas-generation potential. Three microfacies have been identified in the Sakesar Formation at the Tatral section: Bioclastic wacke-packstone, Lockhartia-rich mud-wackestone, and benthic foraminiferal wackestone. The microfacies of the Sakesar Limestone depict the deposition of the Sakesar Limestone from the distal middle ramp to restricted inner ramp settings. Petrophysical well logs analysis of the Sakesar Formation showed an average porosity of ~9.12%; the lithology was identified as limestone, having an average water saturation of ~22.32% and an average hydrocarbon saturation of ~77.68%. Thus indicating average to good reservoir properties with very good hydrocarbon saturation. Sakesar Formation sediments characteristics interpretation showed that it can act as both source rock and reservoir rock in the Potwar Basin.

Use of lipid-lowering drugs in restricted health access settings: Results from the Trends in Drug Utilization During COVID-19 Pandemic in Turkey (PANDUTI-TR) study

BackgroundCOVID-19 restrictions prompted changes in chronic disease management and lifestyle modifications, potentially altering cardiometabolic indicators and lipid-lowering pharmacotherapy patterns. We aimed to assess lipid-lowering drug (LLD) utilization trends during COVID-19 restrictions. MethodsWe obtained nationwide outpatient drug sales and prescribing data for 01.03.2018–31.12.2022 from IQVIA™ Turkey. We evaluated average monthly LLD consumption, their costs, and quarterly prescribing levels in three periods: “before restrictions” (BfR, 01.03.2018–31.03.2020), “during restrictions” (DuR, 01.04.2020–31.03.2022), and “after restrictions” (AfR, 01.04.2022–31.12.2022). Drug utilization was measured via “defined daily dose/1000 inhabitants/day” (DID) metric. ResultsLLD utilization increased from 25.4 ± 3.1 DID in BfR to 36.2 ± 6.8 DID in DuR (p < 0.001), and to 42.6 ± 5.3 DID in AfR (p < 0.001 vs. BfR). Statin consumption significantly rose from 22.0 ± 3.0 DID in BfR to 31.6 ± 6.3 DID in DuR (p < 0.001), and further to 37.6 ± 4.7 DID in AfR (p < 0.01 vs. DuR). High-intensity statin consumption elevated by 115.9% in AfR compared to baseline (p < 0.001). Prescribing of LLDs decreased from 12.5 ± 0.6 DID in BfR to 7.2 ± 1.2 DID in DuR (p < 0.001), later reached 13.6 ± 3.8 DID in AfR (p < 0.001 vs. DuR), with prescribing for ongoing users following similar trend. Expenditure on LLDs increased from €8.4 m ± 0.9 m in BfR to €11.4 m ± 2.0 m in DuR (p < 0.001) and to €12.8 m ± 1.9 m in AfR (p < 0.001 vs. BfR). ConclusionsThis study revealed a surge in consumption of LLDs in Turkey following the onset of the COVID-19 pandemic. This rise might be related to practices facilitating drug access, in addition to potentially greater adherence, or the necessity for more intense pharmacotherapy due to elevated cardiovascular risk.

Sedimentological and sequence stratigraphic analysis of Late Eocene Kirthar Formation, Central Indus Basin, Pakistan, Eastern Tethys

Rakhi Nala (Gorge) section of Eastern Sulaiman Range, Pakistan hosts world-class geological sections from the Cretaceous to the Recent. Here, Late Eocene Kirthar Formation is having a conformable lower contact with the greenish grey massive shale of the Baska Member of the Ghazij Formation. The rusty beds of the Oligocene Chitarwatta Formation are overlying the Kirthar Formation. To understand the detailed microfacies and depositional setting, detailed fieldwork was carried out in the Kirthar Formation in the outer Sulaiman Foldbelt. The late Eocene Kirthar Formation includes Habib Rahi Limestone, Domanda Shale, Pirkoh Limestone, and Drazinda Shale. Domanda Shale Member was deposited in transgression before Habib Rahi Limestone Member was deposited. It was followed up by the deposition of the second member Pirkoh Limestone and Marl Member. Habib Rahi Limestone, Pirkoh Limestone, and Marl Member have catch-up-to-keep-up deposition with rise and fall of sea level. Some facies of Drazinda and Domanda Shales represent a restricted setting supported by the presence of gypsum, while deep sea facies were also identified. This study will provide a guide for evaluating sedimentological concepts and understanding complex facies in highly oil and gas-prone stratigraphic sequences especially in the Eastern Tethys Region.

Practice is the best of all instructors-Effects of enactment encoding and episodic future thinking on prospective memory performance in high-functioning adults with autism spectrum disorder.

Prospective memory (PM) is the ability to remember to carry out intended actions in the future. The present study investigated the effects of episodic future thinking (EFT) and enactment encoding (EE) on PM performance in autistic adults (ASD). A total of 72 autistic individuals and 70 controls matched for age, gender, and cognitive abilities completed a computerized version of the Dresden breakfast Task, which required participants to prepare breakfast following a set of rules and time restrictions. A two (group: ASD vs. controls) by three (encoding condition: EFT vs. EE vs. standard) between-subjects design was applied. Participants were either instructed to engage in EFT or EE to prepare to the different tasks prior to performing the Dresden breakfast or received standard instructions. Analyses of variance were conducted. Autism-spectrum-disorders (ASD) participants did not differ from control participants in their PM performance, regardless of which strategy they used. Compared to the standard condition, EE but not EFT improved time-based PM performance in all participants. This is the first study to find spared time-based PM performance in autistic individuals. The results confirm earlier results of beneficial effects of EE on PM performance. Findings are discussed with regards to the methodology used, sample composition as well as autistic characteristics.

Optimal scheduling of maintenance dredging in a maritime transportation system

A maritime transportation system is a network of ports and commercial terminals connected by navigation channels and navigable inland rivers that enables international trade and the global supply chain. The channels and rivers are subject to recurring sedimentation, which reduces available depths, sailing drafts, and volumes of cargo that vessels can transport between ports. To maintain this network at sufficient depths and enable cost-effective maritime transportation, a specialized fleet of dredging vessels, or dredges, periodically remove accumulated sediment and restore capacity. Scheduling dredges to perform work requires simultaneous consideration of factors specific to the location, dredge, and underlying maritime network and, in practice, often results in significant inefficiencies and delays. Previous models proposed in the literature to optimize dredge scheduling are either intractable or consider only limited aspects of the problem. This paper defines the problem of tactical dredging portfolio scheduling, introduces the General Dredge Scheduling Model (GDSM) as a constraint programming model to solve this problem, and applies GDSM to a realistic problem composed of a portfolio of dredging jobs, fleet of dredges, and sets of seasonal and environmental restrictions.

Sparse subspace clustering in diverse multiplex network model

The paper considers the DIverse MultiPLEx (DIMPLE) network model, where all layers of the network have the same collection of nodes and are equipped with the Stochastic Block Models. In addition, all layers can be partitioned into groups with the same community structures, although the layers in the same group may have different matrices of block connection probabilities. To the best of our knowledge, the DIMPLE model, introduced in Pensky and Wang (2021), presents the most broad SBM-equipped binary multilayer network model on the same set of nodes and, thus, generalizes a multitude of papers that study more restrictive settings. Under the DIMPLE model, the main task is to identify the groups of layers with the same community structures since the matrices of block connection probabilities act as nuisance parameters under the DIMPLE paradigm. The main contribution of the paper is achieving the strongly consistent between-layer clustering by using Sparse Subspace Clustering (SSC), the well-developed technique in computer vision. In addition, SSC allows to handle much larger networks than spectral clustering, and is perfectly suitable for application of parallel computing. Moreover, our paper is the first one to obtain precision guarantees for SSC when it is applied to binary data.

Teaching Artificial Intelligence Good Air Traffic Flow Management

Air traffic flow managers are continually faced with the decision of when and how to respond to predictions of future constraints. The promise of artificial intelligence, and specifically reinforcement learning, to provide decision support in this domain stems from the ability to systematically evaluate a sequence of potential actions, or strategy, across a range of uncertain futures. As decision support for human traffic managers, the generated recommendations must embody characteristics of a good management strategy; doing so requires introducing such notions to the algorithm. This paper proposes inducing stability into the strategy by dynamically constraining the design space based on upstream design decisions to promote consistency in the recommendations over time, where two such constraint sets are considered. The paper further evaluates the impact of adding a performance improvement threshold that must be overcome to accept a new strategy recommendation. The combination of search constraints and threshold values is evaluated against the agent’s reward function in addition to measures proposed to capture the stability of the strategy. The results show that the more restrictive set of constraints yields the best performance in terms of strategy stability and is more likely to reduce the delay where implementation of the threshold has a minor impact on overall performance. However, for the highest impact day of 8 June 2018, applying the threshold reverses the performance gains in delay but dramatically improves the stability of the resulting traffic flow management strategy from a flight level perspective, implying a potential tradeoff between delay optimization and flight predictability.

Hybrid optimization approach for energy minimization in wireless sensor networks leveraging XGBoost and random forest

Wireless Sensor Networks (WSNs) have garnered significant attention from both the academic and industrial communities. However, the limited battery capacity of WSN nodes imposes a set of restrictions on energy dissipations, which has compelled researchers to seek ways to save and minimize energy consumption. This paper presents a hybrid optimization model to minimize energy dissipation in Wireless Sensor Networks (WSNs). Employing linear programming and a combination of XGBoost and Random Forest algorithms, it effectively predicts internode distances and network lifetime. The results demonstrate significant energy savings in WSN deployments, outperforming traditional methods. This approach contributes to the field by offering a practical, energy-efficient strategy for WSN configuration planning, highlighting the model’s applicability in real-world scenarios, where energy conservation is critical.

Comprehensive investigation on fabrication, spectral characterization and biological importance of Co(II) and Ni(II) heteroleptic complexes

Using the microwave irradiation approach, novel metallic complexes of Cobalt [Co(II)] and Nickel [Ni(II)] complexes of 2-aminothiazole (ATZ) and thiocyanate ligands [IUPAC Name: Di(2-aminothiazole) thiocyanato cobalt(II), Di(2-aminothiazole) thiocyanato nickel(II)] were created. The molecular formulas and the geometry of the complexes have been deduced by elemental analysis, molar conductance, magnetic moment, UV–visible, FT-IR, thermogravimetric analysis, cyclic voltammetry, and powder-XRD techniques. Molar conductance values indicate that the complexes are non-electrolyte (1:0) type. The complexes have octahedral structure is revealed by the magnetic moment and UV–visible spectra. The FT-IR spectra reveal that the 2-aminothiazole and thiocyanate ions are coordinated to the metal ion in a monodentate manner. The antibacterial capabilities of ligands and their Co(II) and Ni(II) compounds were studied against a restricted set of microorganisms utilizing the agar-well diffusion method and varying concentrations.When compared to free 2-aminothiazole ligand, the complexes exhibit possible activity against the fungus than bacteria. By assessing how well the complexes and ligands interact with the stable free radical DPPH, their ability to scavenge free radicals has been established. Comparing the complexes to the free ligand, the complexes exhibit greater antioxidant activity due to the fact that metal complexes are widely recognized for their capacity to accelerate medication action and boost therapeutic agent potency when they coordinate with a metal ion. The primary goal of this research is to develop multimodal medicinal drugs, which include studying the roles of 2-aminothiazole and its transition metal complexes.

Few-shot defect classification via feature aggregation based on graph neural network

The effectiveness of deep learning models is greatly dependent on the availability of a vast amount of labeled data. However, in the realm of surface defect classification, acquiring and annotating defect samples proves to be quite challenging. Consequently, accurately predicting defect types with only a limited number of labeled samples has emerged as a prominent research focus in recent years. Few-shot learning, which leverages a restricted sample set in the support set, can effectively predict the categories of unlabeled samples in the query set. This approach is particularly well-suited for defect classification scenarios. In this article, we propose a transductive few-shot surface defect classification method, which using both the instance-level relations and distribution-level relations in each few-shot learning task. Furthermore, we calculate class center features in transductive manner and incorporate them into the feature aggregation operation to rectify the positioning of edge samples in the mapping space. This adjustment aims to minimize the distance between samples of the same category, thereby mitigating the influence of unlabeled samples at category boundary on classification accuracy. Experimental results on the public dataset show the outstanding performance of our proposed approach compared to the state-of-the-art methods in the few-shot learning settings. Our code is available at https://github.com/Harry10459/CIDnet.

Prevalence and risk factors for long COVID among adults in Scotland using electronic health records: a national, retrospective, observational cohort study

Long COVID is a debilitating multisystem condition. The objective of this study was to estimate the prevalence of long COVID in the adult population of Scotland, and to identify risk factors associated with its development. In this national, retrospective, observational cohort study, we analysed electronic health records (EHRs) for all adults (≥18 years) registered with a general medical practice and resident in Scotland between March 1, 2020, and October 26, 2022 (98-99% of the population). We linked data from primary care, secondary care, laboratory testing and prescribing. Four outcome measures were used to identify long COVID: clinical codes, free text in primary care records, free text on sick notes, and a novel operational definition. The operational definition was developed using Poisson regression to identify clinical encounters indicative of long COVID from a sample of negative and positive COVID-19 cases matched on time-varying propensity to test positive for SARS-CoV-2. Possible risk factors for long COVID were identified by stratifying descriptive statistics by long COVID status. Of 4,676,390 participants, 81,219 (1.7%) were identified as having long COVID. Clinical codes identified the fewest cases (n=1,092, 0.02%), followed by free text (n=8,368, 0.2%), sick notes (n=14,469, 0.3%), and the operational definition (n=64,193, 1.4%). There was limited overlap in cases identified by the measures; however, temporal trends and patient characteristics were consistent across measures. Compared with the general population, a higher proportion of people with long COVID were female (65.1% versus 50.4%), aged 38-67 (63.7% versus 48.9%), overweight or obese (45.7% versus 29.4%), had one or more comorbidities (52.7% versus 36.0%), were immunosuppressed (6.9% versus 3.2%), shielding (7.9% versus 3.4%), or hospitalised within 28 days of testing positive (8.8% versus 3.3%%), and had tested positive before Omicron became the dominant variant (44.9% versus 35.9%). The operational definition identified long COVID cases with combinations of clinical encounters (from four symptoms, six investigation types, and seven management strategies) recorded in EHRs within 4-26 weeks of a positive SARS-CoV-2 test. These combinations were significantly (p<0.0001) more prevalent in positive COVID-19 patients than in matched negative controls. In a case-crossover analysis, 16.4% of those identified by the operational definition had similar healthcare patterns recorded before testing positive. The prevalence of long COVID presenting in general practice was estimated to be 0.02-1.7%, depending on the measure used. Due to challenges in diagnosing long COVID and inconsistent recording of information in EHRs, the true prevalence of long COVID is likely to be higher. The operational definition provided a novel approach but relied on a restricted set of symptoms and may misclassify individuals with pre-existing health conditions. Further research is needed to refine and validate this approach. Chief Scientist Office (Scotland), Medical Research Council, and BREATHE.

Joint EURADOS-EANM initiative for an advanced computational framework for the assessment of external dose rates from nuclear medicine patients

Abstract Background In order to ensure adequate radiation protection of critical groups such as staff, caregivers and the general public coming into proximity of nuclear medicine (NM) patients, it is necessary to consider the impact of the radiation emitted by the patients during their stay at the hospital or after leaving the hospital. Current risk assessments are based on ambient dose rate measurements in a single position at a specified distance from the patient and carried out at several time points after administration of the radiopharmaceutical to estimate the whole-body retention. The limitations of such an approach are addressed in this study by developing and validating a more advanced computational dosimetry approach using Monte Carlo (MC) simulations in combination with flexible and realistic computational phantoms and time activity distribution curves from reference biokinetic models. Results Measurements of the ambient dose rate equivalent Ḣ*(10) at 1 m from the NM patient have been successfully compared against MC simulations with 5 different codes using the ICRP adult reference computational voxel phantoms, for typical clinical procedures with 99mTc-HDP/MDP, 18FDG and Na131I. All measurement data fall in the 95% confidence intervals, determined for the average simulated results. Moreover, the different MC codes (MCNP-X, PHITS, GATE, GEANT4, TRIPOLI-4®) have been compared for a more realistic scenario where the effective dose rate Ė of an exposed individual was determined in positions facing and aside the patient model at 30 cm, 50 cm and 100 cm. The variation between codes was lower than 8% for all the radiopharmaceuticals at 1 m, and varied from 5 to 16% for the face-to face and side-by-side configuration at 30 cm and 50 cm. A sensitivity study on the influence of patient model morphology demonstrated that the relative standard deviation of Ḣ*(10) at 1 m for the range of included patient models remained under 16% for time points up to 120 min post administration. Conclusions The validated computational approach will be further used for the evaluation of effective dose rates per unit administered activity for a variety of close-contact configurations and a range of radiopharmaceuticals as part of risk assessment studies. Together with the choice of appropriate dose constraints this would facilitate the setting of release criteria and patient restrictions.

Cosmology in Lorentzian Regge calculus: causality violations, massless scalar field and discrete dynamics

We develop a model of spatially flat, homogeneous and isotropic cosmology in Lorentzian Regge calculus, employing four-dimensional Lorentzian frusta as building blocks. By examining the causal structure of the discrete spacetimes obtained by gluing such four-frusta in spatial and temporal direction, we find causality violations if the sub-cells connecting spatial slices are spacelike. A Wick rotation to the Euclidean theory can be defined globally by a complexification of the variables and an analytic continuation of the action. Introducing a discrete free massless scalar field, we study its equations of motion and show that it evolves monotonically. Furthermore, in a continuum limit, we obtain the equations of a homogeneous scalar field on a spatially flat Friedmann background. Vacuum solutions to the causally regular Regge equations are static and flat and show a restoration of time reparametrisation invariance. In the presence of a scalar field, the height of a frustum is a dynamical variable that has a solution if causality violations are absent and if an inequality relating geometric and matter boundary data is satisfied. Edge lengths of cubes evolve monotonically, yielding a contracting or an expanding branch of the Universe. In a small deficit angle expansion, the system can be deparametrised via the scalar field and a continuum limit of the discrete theory can be defined which we show to yield the relational Friedmann equation. These properties are obstructed if higher orders of the deficit angle are taken into account. Our results suggest that the inclusion of timelike sub-cells is necessary for a causally regular classical evolution in this symmetry restricted setting. Ultimately, this works serves as a basis for forthcoming investigations on the cosmological path integral within the framework of effective spin foams.

Specific features of a scaffolding antibody light chain.

The antigen-binding sites in conventional antibodies are formed by hypervariable complementarity-determining regions (CDRs) from both heavy chains (HCs) and light chains (LCs). A deviation from this paradigm is found in a subset of bovine antibodies that bind antigens via an ultra-long CDR. The HCs bearing ultra-long CDRs pair with a restricted set of highly conserved LCs that convey stability to the antibody. Despite the importance of these LCs, their specific features remained unknown. Here, we show that the conserved bovine LC found in antibodies with ultra-long CDRs exhibits a distinct combination of favorable physicochemical properties such as good secretion from mammalian cells, strong dimerization, high stability, and resistance to aggregation. These physicochemical traits of the LCs arise from a combination of the specific sequences in the germline CDRs and a lambda LC framework. In addition to understanding the molecular architecture of antibodies with ultra-long CDRs, our findings reveal fundamental insights into LC characteristics that can guide the design of antibodies with improved properties.